A PPI analysis starts with an ROI and a design
matrix. It's a way of searching among all other voxels in the brain
(outside the seed ROI) for regions that are highly connected to that
seed. One of the most straightforward ways of doing connectivity
analyses would be to start with one ROI and simply measure the
correlation of all other voxels in the brain to that voxel's timeseries,
looking for high correlation values. As Friston and other pointed out a
while ago, though, it's not quite as interesting if the correlation
between two regions is totally static across the experiment - or if it's
driven by the fact that they're both totally non-active during rest
conditions, say. What might be more interesting is if the connection
strength between a voxel and your seed ROI varied with the
experiment - i.e., there was a much tighter connection during condition A
between these regions than there was during condition B. That may tell
you something about how connectivity influences your actual task (and
vice versa).
PPIs are relatively simple to perform; you
extract the timeseries from a seed voxel or ROI/VOI and convolve it with a
vector representing a contrast in your design matrix (say, A vs. B).
You then put this new PPI regressor into a general linear model
analysis, along with the timeseries itself and the vector representing
your contrast; you'll use those to soak up the variance from the main
effects, which you'll ignore in favor of the PPI interaction term. When
you estimate the parameters of this new GLM, the voxels where the PPI
regressor has a very high parameter are those who showed a signficant
change in connectivity with your experimental manipulation.
PPIs are good to do if you have one ROI of
interest and want to see what's connected with it. They're tricky to
interpret, and they can take a really long time to re-estimate if you
have several ROIs to explore and many subjects.
Jung D, Sul S and Kim H (2013) Dissociable neural processes underlying risky decisions for self versus other. Front. Neurosci. 7:15. doi: 10.3389/fnins.2013.00015
http://mindhive.mit.edu/book/export/html/58
